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Lakes receive and actively process terrestrial dissolved organic matter (DOM) and play an important role in the global carbon cycle. Urbanization results in elevated inputs of nonpoint-source DOM to headwater streams. Retention of water in lakes allows time for alteration and transformation of the chemical composition of DOM by microbes and UV radiation. Yet, it remains unclear how anthropogenic and natural drivers impact the composition and biolability of DOM in non-pristine lakes. We used optical spectroscopy, Fourier transform ion cyclotron mass spectrometry, stable isotopic measurements, and laboratory bioincubations to investigate the chemical composition and biolability of DOM across two large data sets of lakes associated with a large gradient of urbanization in lowland Eastern China, encompassing a total of 99 lakes. We found that increased urban land use, gross domestic products, and population density in the catchment were associated with an elevated trophic level index, higher chlorophyll-a, higher bacterial abundance, and a higher amount of organic carbon with proportionally higher contribution of aliphatic and peptide-like DOM fractions, which can be highly biolabile. Catchment areas, water depth, lake area: catchment area, gross primary productivity, δ18O-H2O, and bacterial abundance, however, had comparatively little linkage with DOM composition and biolability. Urban land use is currently intensifying in many developing countries, and our results anticipate an increase in the level of biolabile aliphatic DOM from nonpoint sources and accelerated carbon cycling in lake ecosystems in such regions.

The 21st century expansion of built-up areas due to rapid urbanization has recently been at the forefront of global land use/land cover research. Knowledge of the changing dynamics of urban land use is crucial for the monitoring of urbanization and the promotion of sustainable urban development. In this paper, Zhejiang Province was selected as the study area. It is a region with rapid urban growth located along the southeastern coast of China, with a highly developed economy but with a shortage of land resources. We employed remotely sensed and socio-economic panel data for the period between 1990 and 2020 to monitor urban land use changes and utilized the spatial Durbin model (SDM) to examine the urbanization process and the various driving factors of rapid urban expansion in Zhejiang Province, China, from 1990 to 2020. The study’s results revealed substantial urban growth of about 6899.59 km2, i.e., 6.6%, whereas agricultural land decreased by 4320.68 km2, i.e., 4.19%. The rapid urban development was primarily attributed to the transformation of farmlands, forestlands, and water bodies into built-up areas by nearly 86.9%, 6.94%, and 6.06%, respectively. The built-up areas revealed features of spatial clustering. The study showed that the expansion hotspots were mainly distributed within the urban fabric of cities such as Hangzhou, Ningbo, Jinhua–Yiwu, and Wenzhou–Taizhou. The results further revealed the substantial influence of urban growth on the local areas of the province. As the core explanatory variables, population and economic development significantly promoted local urban expansion. The study’s findings indicated a positive spatial spillover effect as regards the influence of economic development on the study area’s urban growth, whereas the spatial spillover effect of the population was negative. Therefore, economic development was a major driving factor contributing immensely to the expansion of urban areas in Zhejiang Province, especially in the 26 mountainous counties of the province. The study enriches our understanding of the transformation of LULC and the changing dynamics of urban areas in China and provides the necessary research data that are vital for urban land-use planners and decision-makers to overcome the negative consequences of the expansion of urban areas due to the continuous economic growth of China.

This paper analyzes the impact of urban land-use changes on farmers' livelihood around the city of Bahir Dar (Ethiopia). Rapid urban expansion in and around the city has resulted in massive land-use changes in its urban fringes, with land expropriation programs affecting communities' livelihood and the environment. A survey was conducted in three urbanizing villages near Bahir Dar, focusing on 150 farmers who were land-expropriated and 180 farmers who were non-land-expropriated. Regression models and propensity matching scoring are applied to examine the livelihood differences of farmers in terms of farm income, off-farm income, primary expenditure type, and perception of urban expansion benefits to farmers. The results reveal that land expropriation in the area has led to (a) a shift to off-farm income for land expropriated farmers; (b) an increase in their household expenditure on staple foods compared to other expenditure types, including farm inputs; and (c) diverging perceptions on whether and how city expansion benefits farmers in the neighboring villages. Our findings provide insight into the need for tighter and impactful policy actions to ensure the sustainability of urbanization through accommodating expropriated farmers' livelihood changes and protecting natural resources in the area.

With the advancement of urbanization, the contradiction in the man–land relationship becomes more and more difficult to ignore. Investigation of the change in urban land use, population distribution and its mechanism can provide powerful assistance for urban planning. Since the changes in urban land use and population distribution is a complex process with spatial heterogeneity, the current methods for describing them are still lacking in both interpretability and spatial differences. In this paper, we combine the expansion phenomena of urban land use and population distribution with the heat equation to understand the mechanism. The particle swarm optimization (PSO) algorithm is used to identify the diffusion coefficient to obtain the diffusion law in the city’s development. In this way, the diffusion coefficient identified from the data is directly associated with urban changes. The mechanism of changes in urban land use and population distribution can be explained with the diffusion equation and the diffusion coefficient. Our model is first validated on land use and land cover data, followed by further refinement of the spatial differences in the artificial impervious surface data. The experiment’s results imply that by applying the model to the population data, the model’s generalization ability has been significantly improved.

Kıyı yerleşimleri dinamik ve karmaşık bir yapıya sahip olup, beşeri ve kısmen doğal nedenlerle sürekli değişime uğramaktadır. Bu kapsamda kıyı kentlerini fiziki olarak değişime uğratan nedenlerin bilinmesi ve bu veriler ışığında geleceğe yönelik planlar oluşturulması önemlidir. Doğu Karadeniz kıyı yerleşimlerinde fiziki alan değişimini etkileyen faktörlerin belirlenmesi, Çayeli ilçesinin geçmişten günümüze 4 farklı dönemde değişim yönünün ve hızının belirlenmesi, doğru ve etkili plan kararlarının oluşturulması amaçlanmıştır. Çalışma alanı, Doğu Karadeniz Kıyı Bölgesinde engebeli ve topoğrafik yapıya sahip Rize ili Çayeli ilçesidir. Çalışmada, Doğu Karadeniz kıyı yerleşimlerinde fiziki alan kullanım değişimini etkileyen faktörler belirtilmiş ve Rize ili Çayeli ilçesinin zamansal ve mekânsal fiziki alan değişimi (7 arazi kullanım parametresi) süreci (CBS) belirlenmiştir. Zamansal değişimler, hava fotoğrafları, uydu görüntüleri, topoğrafik haritalar ile CBS kullanılarak tespit edilmiştir. Bu kapsamda yaşanabilir ve sürdürülebilir bir kıyı kenti oluşturmak için kentin gelişimine yönelik yeni planlama ve tasarım senaryolarına yer verilmiştir. Sürdürülebilir bir çevre oluşturmak adına, enerjinin etkin kullanıldığı, insan odaklı, kültürel ve tarihi değerlerin korunduğu, kentsel alanların uzun vadeli planlarla geliştirildiği ve bu süreçte kent içi tarım alanları ve açık yeşil alanların korunduğu bir kent için bu çalışmalar bir altlık oluşturmaktadır., Coastal settlements have a dynamic and complex structure, and are constantly changing due to human and partially natural reasons. In this context, it is important to know the reasons that physically change the coastal cities and to create future plans in the light of these data. It was aimed the factors affecting the physical land use change in the Eastern Black Sea coastal settlements are specifiedto determine the change speed and to make correct and effective plan decisions of these changes in 4 different periods from the past to the present of Çayeli district. The study area is the Çayeli district of Rize province, which has a rugged and topographic structure in the Eastern Black Sea Coastal Region. In the study, the factors affecting the physical land use change in the Eastern Black Sea coastal settlements are specified and the temporal and spatial physical land use change (7 land use parameters) process (GIS) of Rize province Çayeli district. In this context, new planning and design scenarios for the development of the city are included in order to create a livable and sustainable coastal city. In order to create a sustainable environment, these studies form a basis for a city where energy is used effectively, human-oriented, cultural and historical values are protected, urban areas are developed with long-term plans, and in this process, urban agricultural areas and open green areas are protected.

Urban land use and land cover (LULC) change can be efficiently monitored with high-resolution satellite products for a variety of purposes, including sustainable planning. These, together with machine learning strategies, have great potential to detect even subtle changes with satisfactory accuracy. In this study, we used PlaneScope Imagery and machine learning strategies (Random Forests, Support Vector Machines, Naïve Bayes and K-Nearest Neighbour) to classify and detect LULC changes over the City of Cape Town between 2016 and 2021. Our results showed that K-Nearest Neighbour outperformed other classifiers by achieving the highest overall classification of accuracy (96.54% with 0.95 kappa), followed by Random Forests (94.8% with 0.92 kappa), Naïve Bayes (93.71% with 0.91 kappa) and Support Vector Machines classifiers with relatively low accuracy values (92.28% with 0.88 kappa). However, the performance of all classifiers was acceptable, exceeding the overall accuracy of more than 90%. Furthermore, the results of change detection from 2016 to 2021 showed that the high-resolution PlanetScope imagery could be used to track changes in LULC over a desired period accurately.

Unidad de excelencia María de Maeztu CEX2019-000940-M Meteorological and climate prediction models at the urban scale increasingly require more accurate and high-resolution data. The Local Climate Zone (LCZ) system is an initiative to standardize a classification scheme of the urban landscape, based mainly on the properties of surface structure (e.g., building, tree height, density) and surface cover (pervious vs. impervious). This approach is especially useful for studying the influence of urban morphology and fabric on the surface urban heat island (SUHI) effect and to evaluate how changes in land use and structures affect thermal regulation in the city. This article will demonstrate three different methodologies of creating LCZs: first, the World Urban Database and Access Portal Tools (WUDAPT); second, using Copernicus Urban Atlas (UA) data via a geographic information system (GIS) client directly; and third via Google Earth Engine (GEE) using Oslo, Norway as the case study. The WUDAPT and GEE methods incorporate a machine learning (random forest) procedure using Landsat 8 imagery, and offer the most precision while requiring the most time and familiarity with GIS usage and satellite imagery processing. The WUDAPT method is performed principally using multiple GIS clients and image processing tools. The GEE method is somewhat quicker to perform, with work performed entirely on Google's sites. The UA or GIS method is performed solely via a GIS client and is a conversion of pre-existing vector data to LCZ classes via scripting. This is the quickest method of the three; however, the reclassification of the vector data determines the accuracy of the LCZs produced. Finally, as an illustration of a practical use of LCZs and to further compare the results of the three methods, we map the distribution of the temperature according to the LCZs of each method, correlating to the land surface temperature (LST) from a Landsat 8 image pertaining to a heat wave episode that occurred in Oslo in 2018. These results show, in addition to a clear LCZ-LST correspondence, that the three methods produce accurate and similar results and are all viable options.

Urban land leasing is a land monetization strategy that was introduced in 1991 by the contemporary regime. Since then, urban center slum demolitions and their replacement by high-end commercial buildings and urban peripheral low-cost residential condominium expansions have been common occurrences in Addis Ababa. Land rentiers quote extreme land prices at the city center and relatively low prices towards the periphery. Therefore, it has been hypothesized that urban land supply and land prices are determinant factors for urban land use orientations, which have pushed low-end groups towards the periphery. Therefore, based on a lens of land rent theory, 1524 land lease prices and 1038 randomly selected land parcels using Google Earth were used to evaluate locational trends in land prices and land use orientations, respectively. This study revealed that there are significant variabilities between government benchmark land prices and actual quoted land prices. Because of the high rent gaps at the city center, significant land price quotations were recorded, and this overlaps with the urban center slum demolitions and slum resident resettlements at low-cost residential condominiums in the urban periphery. In the first 5 km from the urban economic center, land prices show a declining trend towards the periphery. The central business district is dominated by slums partially under demolition and high-end commercial buildings, while the periphery is dominated by high-rise low-cost residential condominiums. Therefore, the distance from the city center was found to be an explanatory factor of urban land prices. The contributions of other urban utilities to land prices, such as access to transportation routes, could be a future research area.

A key challenge in urban biodiversity conservation is to understand the drivers that govern the population establishment of different groups of species in different urban ecosystems. Here, we ask whether and to what extent vascular plant species establishment (i.e., the ability to establish self-sustaining populations within a certain time span) is driven by interactions of species functional traits, native status, and the type of ecosystem species occur in, with types of ecosystems distinguished by their degree of ecosystem novelty. To answer this question, we use a dataset of 1,178 vascular plant species occurring in Berlin, Germany that originally had been compiled to substantiate the Berlin Red List of endangered plant species. This dataset classifies native and non-native species into casual and established species based on a minimum of 25 years of expert observation. Whether a species is established or casual is distinguished among four broad types of ecosystems: natural remnant, hybrid, novel immature, and novel mature ecosystems. Moreover, we classify species into those native to Berlin and non-native species (split into archaeophytes and neophytes), and link species to selected functional traits and indicator values. By applying ordinal regression within a Bayesian framework, we show that traits are key drivers of these establishment processes and that the traits that drive species establishment differ across types of ecosystems. While across traits, more established species are present in natural remnants, low canopy height, annual life span, and late end of flowering specifically promote establishment in novel immature ecosystems. In hybrid ecosystems, low canopy height and reproduction by seeds are beneficial traits, with the latter promoting establishment in novel mature ecosystems, too. Traits were less important in predicting species establishment in native as compared to non-native species. All types of ecosystems add to urban biodiversity, and trait analyses refine our knowledge on how they can be supported in doing so on the long term. This can help in sharpening conservation measures.

In this study, we investigate the urban landscape patterns in Wuhan, China based on the land use data in the vector format. Using the approach of landscape metric analysis, we calculate forty-four vector-based landscape metrics and then reduce redundant ones through a combination of Spearman correlation analysis and factor analysis, in order to extract a core set of characterizing landscape metrics. We find that the urban landscape can be depicted by six factors including the overall shape and diversity, mean proximity, overall area variation, fragmentation variation, elongation variation, and mean shape complexity. After analyzing typical patterns indicated by the core metrics and the spatial distribution of land use patterns, we compare our findings with other studies and discuss how the core metrics coincide and differ.

There has been considerable interest in the impact that the built environment has on vehicle miles traveled (VMT). While this issue has been extensively researched, due to the heavy reliance on cross-sectional data, there remains uncertainty regarding how effective local land-use planning and regulation might be in reducing VMT. Based on a 13-year panel of Florida counties, models are estimated that relate VMT to new measures of the spatial distribution of alternative land uses within counties and county urban expansion. Identification of causal effects is established by including year and county fixed effects, along with an extensive set of control variables, and instrumenting those land uses that may be endogenous. Incremental annual changes in the spatial concentration of alternative land uses are found to affect VMT. The policy implication is that appropriate land-use policy can reduce VMT and should be considered part of the strategy for dealing with the problem of global warming.

Urban land use on a building instance level is crucial geo-information for many applications, yet difficult to obtain. An intuitive approach to close this gap is predicting building functions from ground level imagery. Social media image platforms contain billions of images, with a large variety of motifs including but not limited to street perspectives. To cope with this issue this study proposes a filtering pipeline to yield high quality, ground level imagery from large social media image datasets. The pipeline ensures that all resulting images have full and valid geotags with a compass direction to relate image content and spatial objects from maps. We analyze our method on a culturally diverse social media dataset from Flickr with more than 28 million images from 42 cities around the world. The obtained dataset is then evaluated in a context of 3-classes building function classification task. The three building classes that are considered in this study are: commercial, residential, and other. Fine-tuned state-of-the-art architectures yield F1-scores of up to 0.51 on the filtered images. Our analysis shows that the performance is highly limited by the quality of the labels obtained from OpenStreetMap, as the metrics increase by 0.2 if only human validated labels are considered. Therefore, we consider these labels to be weak and publish the resulting images from our pipeline together with the buildings they are showing as a weakly labeled dataset.

The quantitative urban system structure in historical periods and the long time-scale urban land area grid dataset with spatial attributes are important for land use and land cover change (LUCC) research. In this study, we aimed to measure the area of county level and above cities in mainland China in the 1930s, also known as the traditional Chinese era (TCE), using a geographic information system (GIS) model and 1:50,000 military topographic maps. Furthermore, we aimed to identify the structure and characteristics of the urban system in the TCE according to the administrative area division using methods such as the rank size law. The results of this study revealed that 1265 county level and above cities existed in the TCE, including 25 provincial level or above cities, 179 prefectural level cities, and 1061 county level cities. The total land area of all of the cities was 1396.48 km2, with a mean value of 1.1 km2 and a standard deviation of 2.37 km2. The rank-size analysis indicated that the urban system in TCE was characterized by large cities with insignificant development (q = 0.829 < 1, R2 = 0.905). The results of the Lorenz curve and Moran analyses showed that the spatial distribution of the urban systems in China during the traditional period exhibited nonuniform agglomeration. Large-scale military topographic maps of historical periods have proven to be a good source for land use reconstruction. The 1° × 1° grid urban land area dataset constructed based on a GIS model in the TCE is important for future research on historical LUCC and can provide basic data for climate change models, urban economic history, and other disciplines.

Urban development is a process that becomes increasingly complex as the city evolves and in which unexpected events can happen which may alter the envisaged trend over time. To anticipate and examine the sudden emergence of processes that are difficult to predict over long-term future timelines, prospective methodologies are required to manage and implement disruptive narrative storylines in future scenario planning. In this research, a method that combines Land Parcel Cellular Automata (LP-CA) and participatory approaches was developed in order to generate land use trajectories that are spatially consistent with disruptive narrative storylines. The urban-industrial corridor of Henares (Spain), which has undergone important urban transformations in recent decades, was chosen as the study area to test the model. In a preliminary validation of the LP-CA model, a Figure of Merit (FOM) value of 0.2817 indicated satisfactory performance. The results demonstrated the usefulness of the participatory scenario-building and the workshop in supporting the configuration of the model parameters and the spatial representation of complex urban dynamics. In conclusion, this methodology can be used to generate simulations of urban land use change in disruptive future scenarios and to spatially observe the propagation of the uncertainty associated with future events across different urban land uses., This work was supported by the Spanish Ministry of Science, Innovation and Universities and the European Social Fund [grant number PRE2018–084663]; the Spanish Ministry of Economy and Competitiveness [TRANSURBAN Project CSO2017–86914-C2–1-P]; and the “Estímulo a la Excelencia para Profesores Universitarios Permanentes” research programme funded by the University of Alcal´a and the Regional Government of Madrid [grant number EPU-INV/2020/009].

This study is dedicated to modeling the spatial variation in COVID-19 prevalence using the adaptive neuro-fuzzy inference system (ANFIS) when dealing with nonlinear relationships, especially useful for small areas or small sample size problems. We compiled a broad range of socio-demographic, environmental, and climatic factors along with potentially related urban land uses to predict COVID-19 prevalence in rural districts of the Golestan province northeast of Iran with a very high-case fatality ratio (9.06%) during the first year of the pandemic (2020–2021). We also compared the ANFIS and principal component analysis (PCA)-ANFIS methods for modeling COVID-19 prevalence in a geographical information system framework. Our results showed that combined with the PCA, the ANFIS accuracy significantly increased. The PCA-ANFIS model showed a superior performance (R2 (determination coefficient) = 0.615, MAE (mean absolute error) = 0.104, MSE (mean square error) = 0.020, and RMSE (root mean square error) = 0.139) than the ANFIS model (R2 = 0.543, MAE = 0.137, MSE = 0.034, and RMSE = 0.185). The sensitivity analysis of the ANFIS model indicated that migration rate, employment rate, the number of days with rainfall, and residential apartment units were the most contributing factors in predicting COVID-19 prevalence in the Golestan province. Our findings indicated the ability of the ANFIS model in dealing with nonlinear parameters, particularly for small sample sizes. Identifying the main factors in the spread of COVID-19 may provide useful insights for health policymakers to effectively mitigate the high prevalence of the disease.

Rapid urban growth has historically led to changes in land use patterns and the degradation of natural resources and the urban environment. Uncontrolled growth of urban areas in the city of Quito has continued to the present day since 1960s, aggravated by illegal or irregular new settlements. The main objective of this paper is to generate spatial predictions of these types of urban settlements and land use changes in 2023, 2028 and 2038, applying the Dinamica EGO cellular automata and multivariable software. The study area was the Machachi Valley between the south of the city of Quito and the rural localities of Alóag and Machachi. The results demonstrate the accuracy of the model and its applicability, thanks to the use of 15 social, physical and climate predictors and the validation process. The analysis of the land use changes throughout the study area shows that urban land use will undergo the greatest net increase. Growth in the south of Quito is predicted to increase by as much as 35% between 2018 and 2038 where new highly vulnerable urban settlements can appear. Native forests in the Andes and forest plantations are expected to decline in the study area due to their substitution by shrub vegetation or agriculture and livestock land use. The implementation of policies to control the land market and protect natural areas could help to mitigate the continuous deterioration of urban and forest areas.

International audience; Collembola have been proposed for several decades as a good model organisms to survey soil biodiversity; but most of the studies focused on taxonomic endpoints. The main objectives of this study are to compare the effects of the different land uses, including urban and industrial land uses, while using both collembolan functional and taxonomic biodiversity approaches. We collected data on 3,056 samples of Collembola communities across 758 sites in various land uses throughout France. The types of land use considered included all types of human activity from forestry to urban, industrial, traffic, mining and military areas, agricultural grassland, arable land, vineyards and urban vegetable gardens. In order to study functional and taxonomic biodiversity, we used community-weighted means, functional indices, species richness and density. When looking at collembolan functional diversity, urban and industrial soils appear clearly less diversified than when considering the taxonomic diversity. We suspect here a functional homogenization effect commonly reported in the literature for various organisms in urban ecosystems. Our study provides range of values for different taxonomic and functional indices of Collembola communities in a wide land use classification across France.

In 2013 the urban authority for the city of Sao Paulo, Brazil, was interested in incorporating environmental aspects into the urban licensing process of diverse urban developments. To overcome concepts related simply to soil sealing, the initiative gave rise to a wide range of principles associated with environmental services and the consideration that green areas in this megacity are unequally distributed. Given the costs involved in analyzing each case, as well as the legal uncertainty among entrepreneurs it has become a tradition in Brazil for authorities in charge of urban licensing to follow general regulations rather than case-by-case studies, except in high impact developments. In response, the Sao Paulo municipal government developed during the period from 2013 to 2016 a governing instrument to deal with these issues, known as the Environmental Quota (EQ). For that, the following guiding principles were established: a) it should have a solid theoretical basis, with incentives for consistent public participation; b) it should be flexible in such a way that it can provide a general framework within which a project designer can make decisions, rather than a set of rigidly determined solutions; and c) it should consider inequalities in the availability of urban green infrastructure throughout the city. This paper will first detail the political-institutional context in which the EQ and its guidelines were established and implemented, then provide a general overview of the tool and the theoretical frameworks within which it was developed, and, finally, discuss the complex social decision-making process of its legal constraints. Moreover, it is analyzed the implementation and application of the EQ to examine its effectiveness and how it relates to the gentrification of the city. Furthermore, it is considered the replicability potential of the EQ as a way to expand both the supply and distribution of green infrastructure and environmental services throughout the urban environment and, thus, contribute toward mitigating the preciousness of urban environments in the Global South.

An informative training set is necessary for ensuring the robust performance of the classification of very-high-resolution remote sensing (VHRRS) images, but labeling work is often difficult, expensive, and time-consuming. This makes active learning (AL) an important part of an image analysis framework. AL aims to efficiently build a representative and efficient library of training samples that are most informative for the underlying classification task, thereby minimizing the cost of obtaining labeled data. Based on ranked batch-mode active learning (RBMAL), this paper proposes a novel combined query strategy of spectral information divergence lowest confidence uncertainty sampling (SIDLC), called RBSIDLC. The base classifier of random forest (RF) is initialized by using a small initial training set, and each unlabeled sample is analyzed to obtain the classification uncertainty score. A spectral information divergence (SID) function is then used to calculate the similarity score, and according to the final score, the unlabeled samples are ranked in descending lists. The most “valuable” samples are selected according to ranked lists and then labeled by the analyst/expert (also called the oracle). Finally, these samples are added to the training set, and the RF is retrained for the next iteration. The whole procedure is iteratively implemented until a stopping criterion is met. The results indicate that RBSIDLC achieves high-precision extraction of urban land use information based on VHRRS; the accuracy of extraction for each land-use type is greater than 90%, and the overall accuracy (OA) is greater than 96%. After the SID replaces the Euclidean distance in the RBMAL algorithm, the RBSIDLC method greatly reduces the misclassification rate among different land types. Therefore, the similarity function based on SID performs better than that based on the Euclidean distance. In addition, the OA of RF classification is greater than 90%, suggesting that it is feasible to use RF to estimate the uncertainty score. Compared with the three single query strategies of other AL methods, sample labeling with the SIDLC combined query strategy yields a lower cost and higher quality, thus effectively reducing the misclassification rate of different land use types. For example, compared with the Batch_Based_Entropy (BBE) algorithm, RBSIDLC improves the precision of barren land extraction by 37% and that of vegetation by 14%. The 25 characteristics of different land use types screened by RF cross-validation (RFCV) combined with the permutation method exhibit an excellent separation degree, and the results provide the basis for VHRRS information extraction in urban land use settings based on RBSIDLC.

China, in a rapid urbanization process, is accompanied by the expansion of built-up land, population accumulation, and intensive land investment, while the improvement of the urban environment cannot keep up with the population and economic density growth. Therefore, the purpose of this paper is to discuss the balance between urban land use economic intensity (built-up area density, population density, land price) and ecological intensity (the depth, breadth, and integration of ecological spaces) in Tangshan. From the perspective of functional zones (FZs), the trade-off of economic and ecological intensity of different types of land use is detailed from the evaluation system. Finally, this paper explores a common intensive development model for different FZs to reach both reasonable economic and ecological purposes in Tangshan. The upshot indicates that the economic and ecological intensity of all FZs in Tangshan follows an opposite spatial distribution, and different types of FZ own their characteristics. The Gini index and Theil index for measuring the typical FZ indicate the unbalanced state between ecological spaces and population in residential areas. To sum up, a resource-based city, such as Tangshan, in the rapid development stage of urbanization tends to pay more attention to economic output and neglect ecological benefits.

The increasing anthropogenic aerosols (AAs) over East Asia have caused significant regional climate responses, but the role of urban land-use changes which occur simultaneously, in altering these AA-induced changes, is not well understood. Here, the modulation of the AAs’ effect on the East Asian winter (November–January) climate by the urban cover in eastern China was investigated using the Community Atmosphere Model version 5.1 coupled with the Community Land Model version 4. Results show that the winter sulfate aerosol burden is higher from central eastern China to southern Japan in the case with the presence of urban cover than in the case without it, resulting from urban-induced circulation changes. Such aerosol changes markedly increase the cloud fraction and precipitation over northern China and the adjacent ocean to the east, especially convection activities around southern Japan. This leads to a cooling effect near the surface over northern China and in the mid-upper troposphere to the east due to aerosol direct and indirect effects. The resulting circulation responses act to shift the mid-tropospheric East Asian trough southward and the upper-level East Asian westerly jet-stream as well, further supporting the surface changes. These winter climate responses to the urban-modulated aerosols can largely offset or even reverse those to the AAs forcing without the urban cover in the model, especially in northern East Asia. This study highlights the need to consider the modulating role of urban land-use changes in assessing the AAs’ climatic effect over East Asia and other regions.

Information about urban land use is important for urban planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability of remotely sensed (RS) data and the development of new methods for data integration to provide new opportunities for mapping types of urban land use. However, the modes of RS and GBD integration are diverse due to the differences in data, study areas, classifiers, etc. In this context, this study aims to summarize the main methods of data integration and evaluate them via a case study of urban land use mapping in Hangzhou, China. We first categorized the RS and GBD integration methods into decision-level integration (DI) and feature-level integration (FI) and analyzed their main differences by reviewing the existing literature. The two methods were then applied for mapping urban land use types in Hangzhou city, based on urban parcels derived from the OpenStreetMap (OSM) road network, 10 m Sentinel-2A images, and points of interest (POI). The corresponding classification results were validated quantitatively and qualitatively using the same testing dataset. Finally, we illustrated the advantages and disadvantages of both approaches via bibliographic evidence and quantitative analysis. The results showed that: (1) The visual comparison indicates a generally better performance of DI-based classification than FI-based classification; (2) DI-based urban land use mapping is easy to implement, while FI-based land use mapping enables the mixture of features; (3) DI-based and FI-based methods can be used together to improve urban land use mapping, as they have different performances when classifying different types of land use. This study provides an improved understanding of urban land use mapping in terms of the RS and GBD integration strategy.

In the era of big data, vast urban mobility data introduce new opportunities to infer urban land use from the perspective of social function. Most existing works only derive land use information from a single type of urban mobility dataset, which is typically biased and results in difficulty obtaining a comprehensive view of urban land use. It remains challenging to fuse high-dimensional and noisy multi-source urban mobility data to infer urban land use. This study aimed to infer urban land use from multi-source urban mobility data using latent multi-view subspace clustering. The variation in the number of origin/destination points over time was initially used to characterize land use types. Then, a latent multi-view representation was applied to construct the common underlying structure shared by multi-source urban mobility data and effectively deal with noise. Finally, based on the latent multi-view representation, the subspace clustering method was used to infer the land use types. Experiments on taxi trajectory data and bus smart card data in Beijing reveal that, compared with the method using a single type of urban mobility dataset and the weighted fusion method, the approach presented in this study obtains the highest detection rate of land use. The urban land use inferred in this study provides calibration and reference for urban planning.

Across Asia, cities are contending with a wide range of food-related issues but most lack a dedicated or coherent set of food policies. For most cities in Asia, food has been a policy and governance blind spot, while national food policy has distinctly lacked an urban perspective. Arguing that food system outcomes are central to the topmost priorities of Asian cities, RICH Food, Smart City calls for cities of all sizes to “get smart to get RICH”—that is, to pursue food policies that foster reliable, inclusive, competitive, and healthy (“RICH”) food systems, better aligned with cities’ contemporary challenges and aspirations. Based on the first systematic survey of urban food policies in 170 Asian cities in 21 countries, RICH Food, Smart City finds that only 8 percent of surveyed cities are “food-smart” and intervene in the food system in ways that are forward-looking, holistic, and inclusive. Nearly three-fourths are either at an early stage of effective engagement or fully in reactive mode, responding to problems as they emerge. Even before the COVID-19 crisis, the need for coherent multisectoral strategies and coordinated action was becoming apparent. The pandemic has now drawn attention to the essential functions of urban food supply chains and businesses and further exposed the vulnerability of urban populations to food insecurity, zoonosis, and foodborne disease. But the crisis has also shown us the potential of the food economy, through its displays of resilience coming from informal marketing channels and budding e-commerce networks and capacities. Rich Food, Smart City illustrates how Asian cities and urban leaders can take on vital food system issues, including food security, diet quality, environmental sustainability, and climate neutrality; and how building RICH food systems can help cities pursue their goals. The book will be of interest to urban planners, policy makers and leaders at the city and national levels, as well as to food system and development practitioners, and others interested in urban food policy and governance.

Collembola have been proposed for several decades as a good model organisms to survey soil biodiversity; but most of the studies focused on taxonomic endpoints. The main objectives of this study are to compare the effects of the different land uses, including urban and industrial land uses, while using both collembolan functional and taxonomic biodiversity approaches. We collected data on 3,056 samples of Collembola communities across 758 sites in various land uses throughout France. The types of land use considered included all types of human activity from forestry to urban, industrial, traffic, mining and military areas, agricultural grassland, arable land, vineyards and urban vegetable gardens. In order to study functional and taxonomic biodiversity, we used community-weighted means, functional indices, species richness and density. When looking at collembolan functional diversity, urban and industrial soils appear clearly less diversified than when considering the taxonomic diversity. We suspect here a functional homogenization effect commonly reported in the literature for various organisms in urban ecosystems. Our study provides range of values for different taxonomic and functional indices of Collembola communities in a wide land use classification across France.

Suburbanization is changing the urban spatial structure and less monocentric metropolitan regions are becoming the new urban reality. Focused only on centers, most works have studied these spatial changes neglecting the role of transport infrastructure and its related location model, the "accessibility city", in which employment and population concentrate in low-density settlements and close to transport infrastructure. For the case of Barcelona, we consider this location model and study the population spatial structure between 1991 and 2006. The results reveal a mix between polycentricity and the accessibility city, with movements away from the main centers, but close to the transport infrastructure.

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Urban land use mapping is critical to understanding human activities in space. The first national mapping result of essential urban land use categories of China (EULUC-China) was released in 2019. However, the overall accuracies in some of the plain cities such as Beijing, Chengdu, and Zhengzhou were lower than 50% because many parcel-based mapping units are large with mixed land uses. To address this shortcoming, we proposed an area of interest (AOI)-based mapping approach, choosing Beijing as our study area. The mapping process includes two major steps. First, grids with different sizes (i.e., 300 m, 200 m, and 100 m) were derived from original land parcels to obtain classification units with a suitable size. Then, features within these grids were extracted from Sentinel-2 spectral data, point of interest (POI), and Tencent Easygo crowdedness data. These features were classified using a random forest (RF) classifier with AOI data, resulting in a 10-category map of EULUC. Second, we superimposed the AOIs layer on classified units to do some rectification and offer more details at the building scale. The overall accuracy of the AOI layer reached 98%, and the overall accuracy of the mapping results reached 77%. This study provides a fast method for accurate geographic sample collection, which substantially reduces the amount of fieldwork for sample collection and improves the classification accuracy compared to previous EULUC mapping. The detailed urban land use map could offer more support for urban planning and environmental policymaking.

La expansión urbana es un proceso espacio-temporal que refleja los patrones de localización de la población y sus actividades, pudiendo provocar impactos irreversibles sobre el territorio. La construcción de narrativas y el posterior cartografiado de escenarios futuros se ha revelado como una estrategia de planificación que puede ayudar en la organización de los usos del suelo y el transporte. El Corredor del Henares (Madrid) se ha escogido como caso de estudio, representando la evolución espacial de cinco usos del suelo urbano en tres escenarios disruptivos para 2050. El presente trabajo tiene como objetivo determinar qué factores explicativos influyeron en la distribución espacial de los cinco usos que se proyectaron en un previo taller de cartografía colaborativa. De esta forma, se pretende obtener información más precisa acerca de los factores explicativos del cambio de uso en cada escenario, que posteriormente pudiera ser aplicable a nuevos modelos espaciales de simulación urbana. Así, dado el carácter ubicuo de los procesos de urbanización, se empleó Regresión Logística Geográficamente Ponderada al permitir un análisis espacial de las relaciones entre los factores explicativos que los modelos globales no permiten estudiar. Los resultados obtenidos mostraron coincidencias entre los factores más significativos del modelo y las narrativas de los escenarios. Urban expansion is a spatio-temporal process that reflects the localization patterns of the population and its activities, which can cause irreversible impacts on the territory. The construction of narratives and the subsequent mapping of future scenarios has been revealed as a planning technique, which can help in the management and planning of land use and transport. The Corridor of Henares (Madrid) has been chosen as a case study, representing the spatial evolution of five urban land uses in three disruptive scenarios for 2050. The present research aims to determine which explanatory factors influenced the process of spatialization of the five uses that was projected in a previous mapping workshop. In this way, the aim is to provide more precise information about the driving factors of use changes in each scenario, which could later be applied to new spatial models of urban simulation. Thus, given the ubiquitous nature of urbanization processes, Geographically Weighted Logistic Regression (GWLR) was used as it enables a spatial analysis of the relationships between the explanatory factors that global models do not allow to study. The outcomes showed coincidences between the most significant factors of the model and the scenario narratives. TRANSURBAN (Simulación de escenarios colaborativos para integrar políticas de transporte urbano sostenible y usos del suelo), financiado por el Ministerio de Economía y Competitividad de España (CSO2017-86914-C2-1-P)

Urban land use prediction is an ill-posed problem from a remote sensing perspective. Some areas are easy to predict with aerial images, e.g. residential areas or industrial areas, whereas it is nearly impossible to predict land use in dense urban centers with highly mixed land use. In this study, we use a fully convolutional, Bayesian neural network for urban land use segmentation that yields predictions and pixel-wise uncertainty values side-by-side. By adding aleatoric uncertainty to the output of our model, we can assess how much the model benefits from the provided data. We train our network using a dataset from four metropolitan areas in the U.S. on two different zoom levels. Our results show that adding aleatoric uncertainty can improve the IoU scores if a sufficient amount of informative data is provided.

Although mapping long-term scenarios can be considered crucial in decision-making, its inclusion in planning processes remains a challenge. This paper aims to gain insights into this by showing a participatory mapping method that creates a dialogue and interaction space between stakeholders and experts. The research was based on three 2050 visions, taking as a case study the eastern sector of the metropolitan area of Madrid, Spain. The method consisted of conducting a participatory mapping workshop involving urban planners, transport engineers, environmental consultants, and property developers. Those experts mapped transformations in urban land use for each 2050 scenario. The results evidenced differences between the three 2050 scenarios, highlighting the usefulness of the participatory mapping workshop to represent the amount and nature of growth in urban land use and organisation of the transport network., This work was supported by the Spanish Ministry of Science, Innovation and Universities and European Social Found, under grant State Programme for the Promotion of Talent and its Employability in R&D (PRE2018-084663). The work was also supported by Spanish Ministry of Economy and Competitiveness under grant TRANSURBAN project (CSO2017-86914-C2-1-P) and the research program ‘Estímulo a la Excelencia para Profesores Universitarios Permanentes’ funded by University of Alcalá and the Community of Madrid (EPU-INV/2020/009).

With more than half of the world's population residing in cities, there has been intense interest in urban agriculture's contribution to sustainability and self-sufficiency. This paper provides new insights into the potential for food production to occur within residential properties. High-resolution optical imagery was employed to quantify residential land immediately available for vegetable production (i.e. residential lawn) in a low-density city. Adelaide (Australia) was used as a case study. Productivity was estimated according to three empirical (low, medium and high) and one commercial yield scenario. Under high and medium yield scenarios, lawn in a typical block exceeded the space required to achieve household self-sufficiency of vegetables. Under the high-yield scenario, 23 % of lawn area would be required to meet the recommended vegetable intake of residents, while under the medium yield scenario, 72 % would be required. This study demonstrates the plausible reality of self-sufficiency through backyard vegetable production. Other resources that might limit production, such as labour, water and nutrients, are explored in the discussion. Urban agriculture is shown to be particularly worthwhile in low density cities; contrasting previous research performed under different urban conditions. However, in this case study, potentially productive land has decreased over time. Refereed/Peer-reviewed

(1) Background: Small- and medium-sized rivers in urban areas are unique environments that serve as blue-green corridors for urban residents. The relationship between land-use types and water quality in these rivers provides important information for effectively addressing urban river restoration and pollution management. However, not much attention has been paid on these small- and medium-sized rivers, especially in large urban agglomerations with dense river networks. (2) Methods: This study undertook a field investigation on 130 sampling small- and medium-sized rivers during the late summer and applied data-driven water quality index and landscape analysis techniques to evaluate the direct impacts of riparian land-use types on the summertime water quality in Shanghai’s small- and medium-sized rivers. Riparian land-use types were derived from OpenStreetMap (OSM) datasets, including industrial, commercial, residential, and green spaces. (3) Results: Residential and green space are located closer to these sampled rivers than industrial and commercial land types, suggesting a tentative link between anthropogenic activities and water quality. Further analysis concluded that urban resident settlements, characterized by specific land-use types, DMSP-OLS nighttime lights, OSM road density, and OSM river density, strongly affected the water quality at the sub-catchment scale. We further determined the critical radii for impacts of land use types on urban rivers. Industrial types may influence water quality within a maximum radius of 5 km, followed by green space (4 km), residential areas (3 km), and commercial developments (2 km). These mathematically and statistically computed radii provide updated visions for river health assessment. For a specific land-use type, the assessed water quality index will be biased by using an assessment area with a radius higher or lower than the above-estimated radii. The study also quantified the spatial extent and transmission efficiency of non-point source pollution in a super built-up area of central Shanghai. We observed that contaminants transported by river pathways can reach a larger area than those transported by roads. (4) Conclusions: The high-quality environments in small- and medium-sized rivers are tightly linked to riparian landscape patterns. It is therefore urgent to control domestic pollutions as part of the restoration of megacity’s urban rivers and grapple with the complex challenges of risks to water supply. This study elaborates the importance of integrating land-use planning and water-quality management to maintain the functions and services of small- and medium-sized urban rivers.

Urban land use mapping is crucial for effective urban management and planning due to the rapid change of urban processes. State-of-the-art approaches rely heavily on the socioeconomic, topographical, infrastructural and land cover information of urban environments via feeding them into ad hoc classifiers for land use classification. Yet, the major challenge lies in the lack of a universal and reliable approach for the extraction and combination of physical and socioeconomic features derived from remote sensing imagery and social sensing data. This article proposes an ensemble-learning-approach-based solution of integrating a rich body of features derived from high resolution satellite images, street-view images, building footprints, points-of-interest (POIs) and social media check-ins for the urban land use mapping task. The proposed approach can statistically differentiate the importance of input feature variables and provides a good explanation for the relationships between land cover, socioeconomic activities and land use categories. We apply the proposed method to infer the land use distribution in fine-grained spatial granularity within the Fifth Ring Road of Beijing and achieve an average classification accuracy of 74.2% over nine typical land use types. The results also indicate that our model outperforms several alternative models that have been widely utilized as baselines for land use classification.

Improvements in battery technology have resulted in small and light electric vehicles (LEVs) appearing on the market in Europe since 2011&mdash, however, their market share is still comparatively low. Low energy requirements and small size can potentially contribute to sustainable mobility in terms of climate protection and reduced local emissions. Our study evaluates how three-wheeled and four-wheeled vehicles, categorised as L-Class according to Regulation (EU) No 168/2013, can contribute to more efficient use of space in urban areas. Evaluations of expert interviews, an extensive literature research, and analyses of the German national household travel survey (MiD) serve as the basis of the study. First, the substitution potential of trips through LEVs is explored using MiD data. Our findings show that between 17% and 49% of trips made and 6% to 30% of the distance covered by private trips can theoretically be substituted by LEVs. Thus, reorganisation of current land use offers potential and additionally, LEVs are an attractive and sustainable addition to other means of transport and contribute to achieving the climate protection goals of the transport sector. Due to the fact that technology application is restricted by travel behaviour and political support, our study discusses possible support by public bodies towards sustainable mobility. Here, the promotion of LEVs in combination with restrictive measures for cars is necessary.

Investigation of urban expansion can provide a better understanding of the urbanization process and its driving forces, which is critical for environmental management and land use planning. Total of 514 sampling points from the aerial photos and field sampling were applied to assess the image accuracy. A Conversion of Land Use and its Effect at Small Region Extent (CLUE-S) model was established to simulate the urbanization process at the township level in the North Xinjiang Economic Zone (NXEZ) of western China. Historical land use and land cover changes with multi-temporal remote sensing data were retrieved, and the underlying driving forces were explored by training the CLUE-S model. Moreover, future changes in urban development were simulated under different scenarios. Results showed that the overall accuracy reaches larger than 80% for the years of 2002, 2005, and 2007, and the corresponding kappa coefficient is bigger than 0.8. The NXEZ is at a premature development stage compared with urban clusters in eastern China. Before 1999, the driving force in this region was primary industry development. In recent years, secondary industries started to show significance in urbanization. These findings indicate that the industrial base and economic development in the NXEZ are still relatively weak and have not taken a strong leading role. When industry and population become the main driving factors, the regional economy will enter a new stage of leap-forward development, which in turn will stimulate a new round of rapid urbanization.

Urban land-use information is important for urban land-resource planning and management. However, current methods using traditional surveys cannot meet the demand for the rapid development of urban land management. There is an urgent need to develop new methods to overcome the shortcomings of conventional methods. To address the issue, this study used the random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models to build machine-leaning methods for urban land-use classification. Taking Hangzhou as an example, these machine-leaning methods could all successfully classify the essential urban land use into 6 Level I classes and 13 Level II classes based on the semantic features extracted from Sentinel-2A images, multi-source features of types of points of interest (POIs), land surface temperature, night lights, and building height. The validation accuracy of the RF model for the Level I and Level II land use was 79.88% and 71.89%, respectively, performing better compared to SVM (78.40% and 68.64%) and ANN models (71.30% and 63.02%). However, the variations of the user accuracy among the methods depended on the urban land-use level. For the Level I land-use classification, the user accuracy was high, except for the transportation land by all methods. In general, the RF and SVM models performed better than the ANN model. For the Level II land-use classification, the user accuracy of different models was quite distinct. With the RF model, the user accuracy of educational and medical land was above 80%. Moreover, with the SVM model, the user accuracy of the business office and educational land classification was above 75%. However, the user accuracy of the ANN model on the Level II land-use classification was poor. Our results showed that the RF model performs best, followed by SVM model, and ANN model was relatively poor in the essential urban land-use classification. The results proved that the use of machine-learning methods can quickly extract land-use types with high accuracy, and provided a better method choice for urban land-use information acquisition.

Understanding distributions of urban land use is of great importance for urban planning, decision support, and resource allocation. The first mapping results of essential urban land use categories (EULUC) in China for 2018 have been recently released. However, such kind of national maps may not sufficiently meet the growing demand for regional analysis. To address this shortcoming, here we proposed a segmentation-based framework named EULUC-seg to improve the mapping results of EULUC at the city scale. An object-based segmentation approach was first applied to generate the basic mapping units within urban parcels. Multiple features derived from high-resolution remotely sensed and social sensing data were updated and then recalculated within each unit. Random forest was adopted as the machine learning algorithm for classifying urban land use into five Level I classes and twelve Level II classes. Finally, an accuracy assessment was carried out based on a collection of manually interpreted samples. Results showed that our derived map achieved an overall accuracy of 87.58% for Level I, and 73.53% for Level II. The accurate and refined map of EULUC-seg is expected to better support various applications in the future.

Urbanization

The composition and arrangement of spatial entities, i.e., land cover objects, play a key role in distinguishing land use types from very high resolution (VHR) remote sensing images, in particular in urban environments. This paper presents a new method to characterize the spatial arrangement for urban land use extraction using VHR images. We derive an adjacency unit matrix to represent the spatial arrangement of land cover objects obtained from a VHR image, and use a graph convolutional network to quantify the spatial arrangement by extracting hidden features from adjacency unit matrices. The distribution of the spatial arrangement variables, i.e., hidden features, and the spatial composition variables, i.e., widely used land use indicators, are then estimated. We use a Bayesian method to integrate the variables of spatial arrangement and composition for urban land use extraction. Experiments were conducted using three VHR images acquired in two urban areas: a Pleiades image in Wuhan in 2013, a Superview image in Wuhan in 2019, and a GeoEye image in Oklahoma City in 2012. Our results show that the proposed method provides an effective means to characterize the spatial arrangement of land cover objects, and produces urban land use extractions with overall accuracies (i.e., 86% and 93%) higher than existing methods (i.e., 83% and 88%) that use spatial arrangement information based on building types on the Pleiades and GeoEye datasets. Moreover, it is unnecessary to further categorize the dominant land cover type into finer types for the characterization of spatial arrangement. We conclude that the proposed method has a high potential for the characterization of urban structure using different VHR images, and for the extraction of urban land use in different urban areas.

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